(a) Field of the Invention
The invention relates to the detection, identification, and diagnosis of lung disease using biomarkers and kits thereof, as well as systems that assist in determining the likelihood of the presence or absence of a disease based on the biomarkers. More specifically, the invention relates to the diagnosis of non-small cell lung cancers and reactive airway diseases by measuring expression levels of specific biomarkers and inputting these measurements into a classification system such as a support vector machine.
(b) Description of the Related Art
Pathologies of Human Lung Tissues
Pathologies of the respiratory system, such as asthma and lung cancer, affect millions of Americans. In fact, the American Lung Association® reports that almost 20 million Americans suffer from asthma. The American Cancer Society, Inc. estimated 229,400 new cancer cases of the respiratory system and 164,840 deaths from cancers of the respiratory system in 2007 alone. While the five year survival rate of all cancer cases when the cancer is detected while still localized is 46%, the five year survival rate of lung cancer patients is only 13%. Correspondingly, only 16% of lung cancers are discovered before the disease has spread. Lung cancers are generally categorized as two main types based on the pathology of the cancer cells. Each type is named for the types of cells that were transformed to become cancerous. Small cell lung cancers are derived from small cells in the human lung tissues, whereas non-small-cell lung cancers generally encompass all lung cancers that are not small-cell type. Non-small cell lung cancers are grouped together because the treatment is generally the same for all non-small-cell types. Together, non-small-cell lung cancers, or NSCLCs, make up about 75% of all lung cancers.
A major factor in the low survival rate of lung cancer patients is the fact that lung cancer is difficult to diagnose early. Current methods of diagnosing lung cancer or identifying its existence in a human are restricted to taking X-rays, Computed Tomography (CT) scans and similar tests of the lungs to physically determine the presence or absence of a tumor. Therefore, the diagnosis of lung cancer is often made only in response to symptoms which have been evident or existed for a significant period of time, and after the disease has been present in the human long enough to produce a physically detectable mass.
Similarly, current methods of detecting asthma are typically performed long after the presentation of symptoms such as recurrent wheezing, coughing, and chest tightness. Current methods of detecting asthma are typically restricted to lung function tests such as spirometry tests or challenge tests. Moreover, these tests are often ordered by the physician to be performed along with a multitude of other tests to rule out other pathologies or reactive airway diseases such as chronic obstructive pulmonary disease (COPD), bronchitis, pneumonia, and congestive heart failure.
Classification Systems
Various classification systems such as machine learning approaches for data analysis and data mining have been widely explored for recognizing patterns and enabling the extraction of important information contained within large data bases in the presence of other information that may be nothing more than irrelevant data. Learning machines comprise algorithms that may be trained to generalize using data with known classifications. Trained learning machine algorithms may then be applied to predict the outcome in cases of unknown outcomes, i.e., to classify data according to learned patterns. Machine learning methods, which include neural networks, hidden Markov models, belief networks and kernel based classifiers such as support vector machines, are useful for problems characterized by large amounts of data, noisy patterns and the absence of general theories.
Many successful approaches to pattern classification, regression and clustering problems rely on kernels for determining the similarity of a pair of patterns. These kernels are usually defined for patterns that can be represented as a vector of real numbers. For example, the linear kernel, radial basis kernel and polynomial kernel all measure the similarity of a pair of real vectors. Such kernels are appropriate when the data can best be represented in this way, as a sequence of real numbers. The choice of kernel corresponds to the choice of representation of the data in the feature space. In many applications, the patterns have a greater degree of structure. These structures can be exploited to improve the performance of the learning algorithm. Examples of the types of structured data that commonly occur in machine learning applications are strings, documents, trees, graphs, such as websites or chemical molecules, signals, such as microarray expression profiles, spectra, images, spatio-temporal data, relational data and biochemical concentrations, amongst others.
Classification systems have been used in the medical field. For example, methods of diagnosing and predicting the occurrence of a medical condition have been proposed using various computer systems and classification systems such as support vector machines. See, e.g., U.S. Pat. Nos. 7,321,881; 7,467,119; 7,505,948; 7,617,163; 7,676,442; 7,702,598; 7,707,134; and 7,747,547, which are hereby incorporated by reference in their entirety. These methods, however, do not provide a high level of accuracy in diagnosing and/or predicting pathologies of human lung tissues such as non-small lung cancer and/or reactive airway disease.
As such, there does not exist in the art a simple, reliable method of diagnosing pathologies of human lung tissues, especially early in their development. Furthermore, there is not a blood test available today capable of indicating the presence of a particular lung tissue pathology. It is therefore desirable to develop a method to determine the existence of lung cancers early in the disease progression. It is likewise desirable to develop a method to diagnose asthma and non-small cell lung cancer, and to differentiate them from each other and from other lung diseases such as infections, before the earliest appearance of clinically apparent symptoms.